{"id":"W2144245426","doi":"10.1109/tnn.2010.2091428","title":"Count Data Modeling and Classification Using Finite Mixtures of Distributions","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multinomial distribution; Dirichlet distribution; Mixture model; Cluster analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Data modeling; Expectation–maximization algorithm; Data mining; Mathematics; Statistics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00677893,0.001227596,0.00270957,0.004964923,0.001243273,0.004087809,0.004879139,0.002820669,0.002117784],"category_scores_gemma":[0.02719173,0.001121104,0.002860039,0.004672697,0.002142547,0.006386254,0.00219842,0.003312555,0.001506564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00230266,"about_ca_system_score_gemma":0.0009970232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005660429,"about_ca_topic_score_gemma":0.005075087,"domain_scores_codex":[0.9952878,0.002006599,0.0002782951,0.001104721,0.001077022,0.0002455334],"domain_scores_gemma":[0.9872392,0.009402744,0.001043529,0.001092017,0.001003705,0.0002187882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002279901,0.0001541197,0.006345289,0.0002510641,0.0001912062,0.0002138414,0.000525798,0.6787812,0.001961188,0.153792,0.002968196,0.154588],"study_design_scores_gemma":[0.000004769968,0.000009710322,0.0002295191,0.00001294195,0.000007661918,0.00003359517,0.00002041706,0.9683954,0.0002204842,0.03056505,0.0004856342,0.00001492468],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006327228,0.0001907068,0.9926433,0.0001590656,0.00002218488,0.00003783704,0.00009194037,0.0002511441,0.0002765888],"genre_scores_gemma":[0.3557582,0.001116319,0.6356862,0.0003346865,0.0003069183,0.0007906984,0.001866726,0.0002326332,0.003907525],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00677893,"threshold_uncertainty_score":0.03585082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06841040994015102,"score_gpt":0.30705565796957,"score_spread":0.2386452480294189,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}